Abstract
The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips, as it is characterised by excellent heat transfer ability and flexibility for miniaturisation. To boost the application of PHP, reliable heat transfer performance evaluation models are especially important. In this paper, a heat transfer correlation was firstly proposed for closed PHP with various working fluids (water, ethanol, methanol, R123, acetone) based on collected experimental data. Dimensional analysis was used to group the parameters. It was shown that the average absolute deviation (AAD) and correlation coefficient (r) of the correlation were 40.67% and 0.7556, respectively. For 95% of the data, the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13 K/W and 40.76 K, respectively. Meanwhile, an artificial neural network model was also proposed. The ANN model showed a better prediction accuracy with a mean square error (MSE) and correlation coefficient (r) of 7.88e-7 and 0.9821, respectively.
Highlights
With the rapid development of the electronics industry, chips are becoming increasingly compact, generating a larger amount of heat in an ever-smaller physical size
Thermal management of chips has and will continue to become one of the most essential technologies. Against this context, pulsating heat pipes, or oscillating heat pipes, characterised by excellent heat transfer ability, simple physical structure, and high flexibility for miniaturisation, are believed to be one of the most promising prospective technologies to meet the requirement of higher heat flux dissipation [2,3]
The temperatures of the evaporation and condensation section can be further obtained based on the thermal resistance and heat transfer correlations [20]
Summary
With the rapid development of the electronics industry, chips are becoming increasingly compact, generating a larger amount of heat in an ever-smaller physical size. There are two basic methods to predict performance of the PHP, classic heat transfer correlations and ANN network models. It was shown that the relative errors of 90% of the data points were less than 5%, and the remaining points lay between 5% and 12% Another ANN model was suggested by Patel et al [19], with inner diameter, outside diameter, lengths of evaporation and condensation section, number of turns, heating power, filling ratio, and inclination angle as the inputs. A more general method was proposed by Wang et al [20] using Kutateladez number, Morton number, Bond number, Prandtl number, Jackob number, the ratio of evaporation length and inner diameter, and the number of turns as selected input parameters for PHP with various working fluids. The temperatures of the evaporation and condensation section can be further obtained based on the thermal resistance and heat transfer correlations [20]
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